from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE     				=		{
    'configuration_longformer': [
        'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LongformerConfig',
        'LongformerOnnxConfig',
    ],
    'tokenization_longformer': ['LongformerTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE     				=		['LongformerTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE     				=		[
        'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'LongformerForMaskedLM',
        'LongformerForMultipleChoice',
        'LongformerForQuestionAnswering',
        'LongformerForSequenceClassification',
        'LongformerForTokenClassification',
        'LongformerModel',
        'LongformerPreTrainedModel',
        'LongformerSelfAttention',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE     				=		[
        'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFLongformerForMaskedLM',
        'TFLongformerForMultipleChoice',
        'TFLongformerForQuestionAnswering',
        'TFLongformerForSequenceClassification',
        'TFLongformerForTokenClassification',
        'TFLongformerModel',
        'TFLongformerPreTrainedModel',
        'TFLongformerSelfAttention',
    ]


if TYPE_CHECKING:
    from .configuration_longformer import (
        LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LongformerConfig,
        LongformerOnnxConfig,
    )
    from .tokenization_longformer import LongformerTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_longformer_fast import LongformerTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_longformer import (
            LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            LongformerForMaskedLM,
            LongformerForMultipleChoice,
            LongformerForQuestionAnswering,
            LongformerForSequenceClassification,
            LongformerForTokenClassification,
            LongformerModel,
            LongformerPreTrainedModel,
            LongformerSelfAttention,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_longformer import (
            TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLongformerForMaskedLM,
            TFLongformerForMultipleChoice,
            TFLongformerForQuestionAnswering,
            TFLongformerForSequenceClassification,
            TFLongformerForTokenClassification,
            TFLongformerModel,
            TFLongformerPreTrainedModel,
            TFLongformerSelfAttention,
        )

else:
    import sys

    _SCREAMING_SNAKE_CASE     				=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->bool:




  '''simple docstring'''

  _UpperCamelCase           =    get_failure_array(a__ )

  # 2) Step through text searching for pattern
  _UpperCamelCase      ,						_UpperCamelCase           =    0, 0  # index into text, pattern
  while i < len(a__ ):
    if pattern[j] == text[i]:
      if j == (len(a__ ) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _UpperCamelCase           =    failure[j - 1]
      continue
    i += 1
  return False


def 				lowerCAmelCase__       (			a__ )      ->list[int]:




  '''simple docstring'''

  _UpperCamelCase           =    [0]
  _UpperCamelCase           =    0
  _UpperCamelCase           =    1
  while j < len(a__ ):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCamelCase           =    failure[i - 1]
      continue
    j += 1
    failure.append(a__ )
  return failure


if __name__ == "__main__":
   # Test 1)
   lowerCamelCase__  =					'''abc1abc12'''
   lowerCamelCase__  =					'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   lowerCamelCase__  =					'''alskfjaldsk23adsfabcabc'''
   assert kmp(pattern, texta) and not kmp(pattern, texta)

   # Test 2)
   lowerCamelCase__  =					'''ABABX'''
   lowerCamelCase__  =					'''ABABZABABYABABX'''
   assert kmp(pattern, text)

   # Test 3)
   lowerCamelCase__  =					'''AAAB'''
   lowerCamelCase__  =					'''ABAAAAAB'''
   assert kmp(pattern, text)

   # Test 4)
   lowerCamelCase__  =					'''abcdabcy'''
   lowerCamelCase__  =					'''abcxabcdabxabcdabcdabcy'''
   assert kmp(pattern, text)

   # Test 5)
   lowerCamelCase__  =					'''aabaabaaa'''
   assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]




